docstr: For each data transformer in the pipeline, first fit the data if transformer is fittable then transform data
  using fitted transformer. The transformed data is then passed to next transformer.
  
  Parameters
  ----------
  data
      (`Sequence` of) `TimeSeries` to fit and transform on.
  
  Returns
  -------
  Union[TimeSeries, Sequence[TimeSeries]]
      Transformed data.
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code: def fit_transform(
      self, data: Union[TimeSeries, Sequence[TimeSeries]]
  ) -> Union[TimeSeries, Sequence[TimeSeries]]:
      """
      For each data transformer in the pipeline, first fit the data if transformer is fittable then transform data
      using fitted transformer. The transformed data is then passed to next transformer.
  
      Parameters
      ----------
      data
          (`Sequence` of) `TimeSeries` to fit and transform on.
  
      Returns
      -------
      Union[TimeSeries, Sequence[TimeSeries]]
          Transformed data.
      """
      for transformer in self._transformers:
          if isinstance(transformer, FittableDataTransformer):
              transformer.fit(data)
  
          data = transformer.transform(data)
      return data
  
file_path: darts/darts/dataprocessing/pipeline.py
imports: from copy import deepcopy
  from typing import Iterator, Sequence, Union
  from darts import TimeSeries
  from darts.dataprocessing.transformers import BaseDataTransformer, FittableDataTransformer, InvertibleDataTransformer
  from darts.logging import get_logger, raise_if_not
label_desc: Use image node_id 3 for calling the Pipeline obj's underlying member method code with example usage: obj.fit_transform(data) and returns: data